Do you still need Language Quality Assurance? A practical guide for localization teams

Localization teams have more ways than ever to manage language quality. But which approaches are actually worth your team’s time and your company’s budget? And do you still need to invest in third-party linguistic quality assurance (LQA) to get top-tier quality?

Moving into 2026, traditional quality workflows don’t always make sense. Suddenly, LQA, the old gold standard, is starting to seem slow and expensive. Everyone wants faster launches, lower costs, and clear ROI, and they are willing to sacrifice a bit on quality to get that.  Teams are starting to handle more with AI and automation and less with manual or human-intensive processes.

To make sense of how localization teams are (and should be) approaching language quality these days, we spoke with Jan Dockal, Language Quality Director at Acclaro. In our conversation, he broke down the six real-world approaches he’s seeing brands use to assess translation quality today.

Spoiler: there’s no one-size-fits-all solution. But there is a smarter way to figure out and implement what your program actually needs.

First: Before you measure quality, you have to define it

Many organizations skip this (or any) quality step simply because they’re underwater or over budget. When you’re managing dozens of languages and stakeholders, creating detailed style guides or quality standards and then doing QA for every market seems like a luxury. But it’s not. In fact, it’s foundational. No matter how you choose to deal with quality, skipping this step can undermine your program.

Jan Dockal sees this all the time: Quality programs that fail not because the translation itself was poorly done, but because it was ill-defined and/or managed against the wrong expectations.

He says, “If you measure something on the wrong scale—let’s say you apply metrics meant for highly creative content on technical content—it’s never going to work,” Jan says. “You get a generic score that’s calculated the same way whether you’re evaluating marketing brand copy or a financial app instead of clearly measuring the needed effect, so it’s just useless.”

Why do so many teams skip this? Jan says it comes down to two things: cost and complexity. Many brands don’t have in-market language leads for every locale who know how to do this. Others don’t have time to create and approve guides across teams.

Without standards, evaluation is subjective. But here’s the flip side: when expectations are clearly defined, it changes everything. Jan explains: “The better the brand is defined in the target language, the lower the risk of this subjective influence. And the better we have defined expectations from our customers, the easier it is for us to fulfill these expectations, and to measure against them.”

While it might make sense to test out a new market without investing heavily in these assets, once you’re committed for the long term, they are vital investments.

Bottom line: If you don’t know what “quality” looks like for your brand and your audience, how can you tell if you’re getting it?

Five real-world approaches to measuring quality

Once your foundation is in place—style guides, measurement standards, clear quality expectations—the next decision is how to evaluate the quality of your translations.

You’ve got options. Instead of following a single playbook, you can choose the right tool for the content and the context.

We asked Jan to walk us through the five quality methods he sees most often in the field, both traditional approaches and new strategies that teams are using to keep up with faster timelines and growing volumes.

1. Third-party linguistic QA (LQA)

Linguistic quality assurance (LQA) is a structured review where professional linguists check translated content for accuracy, grammar, and alignment with brand or style guidelines. It’s often done by a third party to provide an objective evaluation and spot issues internal teams might miss.

LQA programs offer structure, objectivity, and external validation. LQA is of high value for content with high risk, compliance requirements, or brand sensitivity. When well designed, it can drive the success of a high-quality localization process. Over time, it has been the default way to check accuracy and catch errors.

However, traditional LQA has a couple disadvantages for modern localization programs: It can be slow. It adds cost. It can be more than is necessary for all content types. Some translation departments are moving toward a tiered quality system that aligns review effort with content type and risk level.

“Some of the biggest brands are becoming more brave, and they are willing to risk a little bit in the area of quality, as long as the financial gains are worth it,” Jan explains. “For example, a business could embed further in a market because more content is released more quickly.”

2. Automated Quality Estimation (AQE)

AQE uses AI to score the quality of translated content, before or after human post-editing. For teams working with high volumes of machine-translated content, it offers a fast way to monitor trends and flag potential issues without involving additional reviewers.

The promise is speed and scalability. But in practice, Jan Dockal says the results may not align with what brands expect from human review. Either tools aren’t accurate or they aren’t calibrated to a company’s content or standards.  Jan says, “Some customers purchased an AQE solution which was sold to them as a silver bullet because it uses AI but it’s underperforming,” he says. “It doesn’t match their perception of the quality of the same content.”

In one case, a major brand considered abandoning an AQE platform because the scoring consistently conflicted with their internal assessments.

Accuracy of the tool and calibration to internal standards are core challenges. Also, AQE can be helpful for certain languages and straightforward content, but it tends to struggle with things like tone and local context.

Used strategically, though, it can be valuable—especially as a low-lift way to track quality trends, identify possible trouble spots, and reduce load on human reviewers. To sum up, Jan says: “It’s not free, but it’s very fast and it can be reliable,” Jan says. “And then if you see problems, you can always take further steps to verify.”

3. Linguistic and functional testing

Linguistic and functional testing happens after translation, often close to or just after launch.

• Linguistic testing is the review of translated content to catch language issues like grammar, accuracy, or clarity in context.
• Functional testing checks whether the content displays and behaves correctly in its final format, including layout, links, input fields, and character rendering.

These processes both give reviewers a chance to evaluate content in its real-world context—on a live website, inside a product, or within an app.

Jan says more brands are turning to this kind of testing as an alternative to traditional LQA.

Unlike LQA, which typically happens in spreadsheets or static files, these approaches catch problems that only show up when content is rendered in its final form. That includes broken strings, formatting issues, or translations that read awkwardly once they’re on screen.

“If you do not have means to do LQA,” Jan says, “linguistic testing is a great answer.”

The downside is timing. Because testing happens late, there’s often a delay between when an issue is introduced and when it’s caught. That can lead to rework, TM cleanup, or redelivery.

“The time delay is a problem. Low quality translations might already be in the built product and we might be required to do redeliveries and cleanups,” Jan says.

Even so, linguistic and functional testing plays an important role. It surfaces issues that early-stage QA can miss, especially in content where layout, interactivity, or platform matter.

4. Usability testing and customer feedback

Some brands are expanding their view of quality by tapping into customer feedback and in-market usability testing. Shouldn’t the customer be the final arbiter of quality? This method focuses on how well translated content performs when it reaches real users, whether that’s a help article, onboarding flow, website, or product UI.

Unlike linguistic or functional testing, which is typically structured and carried out by professionals, this type of feedback often comes from end users or local teams. This means it’s less formal, less controlled, and harder to standardize. But it can offer insights that structured QA might miss.

Usability feedback helps reveal when content doesn’t do what it’s supposed to: provide clear, actionable and trustworthy information for users. But a big challenge with user feedback is determining relevance. Without clear guidelines, it’s easy to confuse software usability issues or platform bugs for linguistic errors. And because feedback often comes post-launch, it may be difficult to trace or fix without extra effort.

User feedback is especially valuable for AI-generated content, which may be quite fluent, align with your brand style, and sound great to internal reviewers but yet fail to provide your audience with what they need to complete their objectives. For example, a chatbot message could pass a linguistic review for style and accuracy but still confuse users trying to reset a password.

Jan says, “It is a great source of information because it can really help you understand the root cause, which may be functionality issues or confusion on the part of the user and not language problems. When interpreted and actioned correctly, usability data can be extremely useful as an indicator of quality.”

Usability testing works best when it’s intentional, well-scoped, and used to supplement other quality approaches.

5. LSP-led sampling and internal QA reporting

As traditional LQA becomes harder to justify for every project or language, some brands are shifting to lighter, more flexible QA models. One common approach is sampling—where a percentage of delivered content is reviewed by the language service provider (LSP), and the results are reported back to the client.

Jan says this is becoming a popular alternative, especially for high-volume machine translation workflows.

“We agree with customers on regular reporting of quality trends and this gives them reliable and sufficient visibility into the quality they buy,” he explains. “They may not want a full LQA, but they still need some level of control. Customers now have insight into overall quality without requiring a separate vendor or more process overhead.”

This model is often embedded within the translation workflow. For example, a translation vendor might apply post-editing and revision to every job, then sample 10% of those deliveries for deeper review.

It’s not a replacement for full LQA when the stakes are high. But for programs with trained MT engines, fast turnarounds, or a continuous delivery model, it can strike a workable balance between oversight and efficiency. Bonus: it’s also often cheaper than standard LQA.

 6. AI QA

This approach, often via proprietary tools built in-house at enterprises, can be a quick check of vendor produced translations.

When LQA makes sense (and when it doesn’t)

Third-party LQA still plays an important role in translation programs, but it’s not the best fit for every type of content. Jan points out that, “when it’s applied without context or clear objectives, it can create more friction than value.”

That said, it’s still the right choice in high-stakes situations where accuracy matters. For example:

• Legal disclaimers and contract terms
• Regulated product documentation
• Financial onboarding or approval workflows
• Medical content or anything with safety implications

For content that moves quickly or updates often, many teams are choosing lighter QA methods. Jan sees more programs using options like AI scoring, LSP-led sampling, or post-release testing for:

• Product UI or help center articles
• Internal documentation
• High-volume MT content with trained engines

The smartest move? Partnering up

If there’s one thing Jan makes clear, it’s this: no single quality approach works for every brand, every content type, or every market. What matters is choosing the right one for the situation in front of you.

“If someone comes to you and says, ‘We have a single unified solution for all your problems,’ you should run,” Jan says. “They’re selling the same thing to all their customers, regardless of their needs and specifics.”

The right solutions could mean a structured LQA program for regulated content, a sampling model built into your MT workflow, or post-release functional testing for fast-moving UI strings. The key is to stay flexible, focus on outcomes, and work with a partner who can help you sort through the options.

“We don’t want to be the LSP that says we have THE one answer. We want to offer multiple answers,” Jan says. “We truly want to find, together with our customers, a solution that is the right mix for their program, their goals, and their teams.”

Ready to rethink your approach to quality?

Let’s talk about what actually works for your content and your market.